PDF Pattern Extraction in Stock Market data



Master Project

Pattern Extraction in Stock Market data

By Suresh Rajagopal Bachelors in Engineering (1992), Madras University, India Master of Business Administration (2012), Regis University, CO, USA

A Master Project report submitted to the Graduate Faculty of the University of Colorado at Colorado Springs

in paritial fulfillment of the requirements for the degree of Master of Science in Computer Science Department of Computer Science

College of Engineering and Applied Science 2016

? Copyright By Suresh Rajagopal 2016 All Rights Reserved 1

This Report for Master of Science degree by Suresh Rajagopal

has been approved for the Department of Computer Science by _____________________________

Dr. Jugal Kalita _____________________________

Dr. Edward Chow _____________________________

Dr. Thomas Zwirlein

__________________ Date

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Table of Contents

Abstract ............................................................................................................................................................................................4 1. INTRODUCTION ...................................................................................................................................................................5 2. RELATED WORK..................................................................................................................................................................6

Recurrent neural network approach .........................................................................................................................................6 Fast Similarity Search .................................................................................................................................................................6 Support Vector Machines ...........................................................................................................................................................6 Probabilistic approach ................................................................................................................................................................7 Multi-resolution symbolic representation of Time series .........................................................................................................7 Dynamic Time Warping..............................................................................................................................................................8 3. STOCK PATTERNS ...............................................................................................................................................................9

(a) Head and Shoulders pattern............................................................................................................................................10 (b) Inverse Head and Shoulders pattern...............................................................................................................................10 (c) Rectangular patterns.......................................................................................................................................................10 4. METHODOLOGY ................................................................................................................................................................13 Preparation of Data sets............................................................................................................................................................13 Template Pattern Generation ...................................................................................................................................................14 Normalization ............................................................................................................................................................................15 Pattern Search Space.................................................................................................................................................................16 Dynamic Time Warping (DTW)...............................................................................................................................................17 Data Point Reduction ................................................................................................................................................................19 5. EVALUATION ......................................................................................................................................................................20 6. IMPLEMENATION..............................................................................................................................................................23 7. RESULTS ...............................................................................................................................................................................26 8. SIMULATION WITH THINKORSWIM RESULT ..........................................................................................................31 9. CONCLUSION ......................................................................................................................................................................34 References ......................................................................................................................................................................................36

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ABSTRACT

In this paper, we propose an approach to recognize predefined patterns in stock-price time series data to make some investment decisions. The stock-price data for various stocks are first normalized to match the scale of predefined pattern templates for similarity cost calculation between input and the template charts. The pattern of interest may form at different time segments and the search algorithm performs the exhaustive search for the maximum time frame of one year. The Sliding windows of multiple resolutions (time segments) are created, and the pattern within the windows are compared with the template patterns. The cost is computed using the Dynamic Time Warping algorithm, which measures the similarity between the input and the template charts.

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1. INTRODUCTION

THIS project focuses on the identification of various predefined patterns in time series data, an essential function in the technical analysis in stock screening processes. Stock market professionals use sophisticated and costly tools to perform pattern identification in the real world. Individual investors usually do not have the acess to such tools. The objective of this project is to create a usable model to perform pattern recognition using machine learning algorithms. The model is expected to scan the stock market data and provide a list of stocks that has the potential to form certain predefined patterns. There has been a lot of studies by stock market professionals on the price charts [6] , and around 20 time-tested patterns are available for consideration for trading purpose. Some people argue that the prices of stocks are mostly determined by speculations in the market [7]. News about the company, market parameters such as political and economic conditions, and market emotions are some of the common drivers of the price fluctuations [10] in the stock market. However, the standard patterns are formed based on variations in the supply and demand of stocks being traded. Identifying the pattern formation upfront could potentially be a critical step in making the right decision in stock trading. Apart from applying this pattern extraction for stock trading, the same technique can be applied in any kind of time series data to understand patterns and behavior of data and thereby aid the decision making process.

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